code: def test_low_cardinality_enum_limited():
      raw_data = [
          "1|2|A|A",
          "1|2|A|A",
          "1|2|A|A",
          "1|2|A|A",
          "1|2|A|A",
          "2|2|A|B",
          "2|2|A|A",
          "1|4|A|A",
          "1|2|B|A",
          "1|2|B|A",
          "1|2|A|A",
          "1|2|A|A",
          "4|5|C|A",
          "4|5|D|A",
          "2|5|D|A",
          "3|5|E|A",
          "4|5|F|A",
          "4|5|G|A",
          "4|5|H|A",
          "4|5|I|A",
          "4|5|J|A",
          "4|5|K|A",
          "4|5|L|A",
          "4|5|M|A",
          "4|5|N|A",
          "4|5|O|A",
          "4|5|P|A",
      ]
      raw_data = [el.split("|") for el in raw_data]
      df = H2OFrame(raw_data)
      agg = H2OAggregatorEstimator(
          target_num_exemplars=5, categorical_encoding="enum_limited"
      )
      agg.train(training_frame=df)
      assert (
          agg.aggregated_frame is not None
      ), "Trained model should produce not empty aggregated frame"
      assert is_consistent(
          df.nrows, agg.aggregated_frame
      ), "Exemplar counts should sum up to number of training rows"
      # from AggregatorTest.java
      assert (
          agg.aggregated_frame.nrows == 7
      ), "Number of exemplars of this test case should be 7"
  
file_path: h2o-3/h2o-py/tests/testdir_algos/aggregator/pyunit_cat_encoding.py
imports: import h2o
  from tests import pyunit_utils
  from h2o.frame import H2OFrame
  from h2o.estimators.aggregator import H2OAggregatorEstimator
label_desc: Use image node_id 5 for calling a global function with example usage: test_low_cardinality_enum_limited() without return types